| Objective | Chapter |
| Section 1: Designing data processing system | |
| 1.1 Selecting the appropriate storage technologies | 1 |
| 1.2 Designing data pipelines | 2, 3 |
| 1.3 Designing a data processing solution | 4 |
| 1.4 Migrating data warehousing and data processing | 4 |
| Section 2: Building and operationalizing data processing systems | |
| 2.1 Building and operationalizing storage systems | 2 |
| 2.2 Building and operationalizing pipelines | 3 |
| 2.3 Building and operationalizing infrastructure | 5 |
| Section 3: Operationalizing machine learning models | |
| 3.1 Leveraging prebuilt ML models as a service | 12 |
| 3.2 Deploying an ML pipeline | 9 |
| 3.3 Choosing the appropriate training and serving infrastructure | 10 |
| 3.4 Measuring, monitoring, and troubleshooting machine learning models | 11 |
| Section 4: Ensuring solution quality | |
| 4.1 Designing for security and compliance | 6 |
| 4.2 Ensuring scalability and efficiency | 7 |
| 4.3 Ensuring reliability and fidelity | 8 |
| 4.4 Ensuring flexibility and portability | 8 |
